The design and fabrication of products often requires large groups of people with highly specialized skills and knowledge, often numbering in the thousands, and often spread across continents. Furthermore, the development lead-time for some products can easily stretch to many years, especially for large, costly products such as automobiles, boats and homes. The complexity of these products (and in many cases services) and the processes used to develop them, is reflected in the organizational structure of the companies which design and make them. Within the typical product development organization, the stakeholders in a given product development project include such diverse departments as product planning, styling, engineering, sales and marketing, manufacturing, after-sales service, legal affairs, and more recently, external suppliers and business partners. Each of these departments or organizations has its own objectives, constraints, and performance measures, and its executives and managers their own goals and idiosyncrasies. These and other factors magnify the distance between the people who design products and services, and the customers to whom they are marketed, whether the distance is measured in terms of geography, time, and technical knowledge, or in terms of worldview, goals, and daily concerns.
Designing and producing a product incorporating the “voice of the customer” remains fraught with errors and distortions. For example, merely ascertaining the wants and needs of the customer can be difficult and often provides contradictory results. Further, trying to translate those wants and needs into a decision, product, artifact or service while minimizing distortions can be arduous and costly.
Conventional techniques employed by market researchers range from highly qualitative methods borrowed from ethnography, such as open-ended interviewing, participant observation, and focus groups, to highly popular quantitative statistical methods such as survey research and conjoint analysis. More recently, collaborative filtering has been used to predict an individual's affinity for a particular product or service based on their membership in a particular group or similarities with other individuals. As one example, U.S. Patent Application Publication Number 2006/0259344, entitled “Statistical Personalized Recommendation System” by Patel, et al. describes techniques for analyzing personal ratings of and preferences for items that include grouping respondents into cohorts based on various characteristics. Such approaches generally apply standard cohort-wide models (such as hierarchical Bayes estimation or latent class modeling) to individuals in the cohort and attempt to adjust the cohort-specific model that borrow information across multiple groups and groupings. They do not, however generate individual level parameter estimates for a particular individual based on their membership in multiple groups. Furthermore, collaborative filtering techniques do not consider an individual's membership in multiple groups or groupings or the relative extent to which each one of multiple group membership affects an individual's overall predicted affinity for a particular item.
Each of these tools and techniques suffer from various shortcomings such that there remains a need for techniques for deriving superior, individual-specific predictive models based on an individual's membership in multiple groups or groupings.